# Tree representations (skbio.tree)¶

This module provides functionality for working with trees, including phylogenetic trees and hierarchies. Functionality is provided for constructing trees, for traversing in multiple ways, comparisons, fetching subtrees, and more. This module supports trees that are multifurcating and nodes that have single descendants.

## Classes¶

 TreeNode([name, length, parent, children]) Representation of a node within a tree

## Phylogenetic Reconstruction¶

 nj(dm[, disallow_negative_branch_length, …]) Apply neighbor joining for phylogenetic reconstruction.

## Utility Functions¶

 majority_rule(trees[, weights, cutoff, …]) Determines consensus trees from a list of rooted trees

## Exceptions¶

 TreeError General tree error NoLengthError Missing length when expected DuplicateNodeError Duplicate nodes with identical names MissingNodeError Expecting a node NoParentError Missing a parent

## Examples¶

>>> from skbio import TreeNode
>>> from io import StringIO


A new tree can be constructed from a Newick string. Newick is a common format used to represent tree objects within a file. Newick was part of the original PHYLIP package from Joseph Felsenstein’s group (defined here), and is based around representing nesting with parentheses. For instance, the following string describes a 3 taxon tree, with one internal node:

((A, B)C, D)root;

Where A, B, and D are tips of the tree, and C is an internal node that covers tips A and B.

Now let’s construct a simple tree and dump an ASCII representation:

>>> tree = TreeNode.read(StringIO("((A, B)C, D)root;"))
>>> print(tree.is_root()) # is this the root of the tree?
True
>>> print(tree.is_tip()) # is this node a tip?
False
>>> print(tree.ascii_art())
/-A
/C-------|
-root----|          \-B
|
\-D


There are a few common ways to traverse a tree, and depending on your use, some methods are more appropriate than others. Wikipedia has a well written page on tree traversal methods, and will go into further depth than what we’ll cover here. We’re only going to cover two of the commonly used traversals here, preorder and postorder, but we will show examples of two other common helper traversal methods to gather tips or internal nodes.

The first traversal we’ll cover is a preorder traversal in which you evaluate from root to tips, looking at the left most child first. For instance:

>>> for node in tree.preorder():
...    print(node.name)
root
C
A
B
D


The next method we’ll look at is a postorder traveral which will evaluate the left subtree tips first before walking back up the tree:

>>> for node in tree.postorder():
...    print(node.name)
A
B
C
D
root


TreeNode provides two helper methods as well for iterating over just the tips or for iterating over just the internal nodes.

>>> for node in tree.tips():
...    print("Node name: %s, Is a tip: %s" % (node.name, node.is_tip()))
Node name: A, Is a tip: True
Node name: B, Is a tip: True
Node name: D, Is a tip: True

>>> for node in tree.non_tips():
...    print("Node name: %s, Is a tip: %s" % (node.name, node.is_tip()))
Node name: C, Is a tip: False


Note, by default, non_tips will ignore self (which is the root in this case). You can pass the include_self flag to non_tips if you wish to include self.

The TreeNode provides a few ways to compare trees. First, let’s create two similar trees and compare their topologies using compare_subsets. This distance is the fraction of common clades present in the two trees, where a distance of 0 means the trees contain identical clades, and a distance of 1 indicates the trees do not share any common clades:

>>> tree1 = TreeNode.read(StringIO("((A, B)C, (D, E)F, (G, H)I)root;"))
>>> tree2 = TreeNode.read(StringIO("((G, H)C, (D, E)F, (B, A)I)root;"))
>>> tree3 = TreeNode.read(StringIO("((D, B)C, (A, E)F, (G, H)I)root;"))
>>> print(tree1.compare_subsets(tree1))  # identity case
0.0
>>> print(tree1.compare_subsets(tree2))  # same tree but different clade order
0.0
>>> print(tree1.compare_subsets(tree3))  # only 1 of 3 common subsets
0.6666666666666667


We can additionally take into account branch length when computing distances between trees. First, we’re going to construct two new trees with described branch length, note the difference in the Newick strings:

>>> tree1 = \
>>> tree2 = \


In these two trees, we’ve added on a description of length from the node to its parent, so for instance:

>>> for node in tree1.postorder():
...     print(node.name, node.length)
A 0.1
B 0.2
C 0.3
D 0.4
E 0.5
root None


Now let’s compare two trees using the distances computed pairwise between tips in the trees. The distance computed, by default, is the correlation of all pairwise tip-to-tip distances between trees:

>>> print(tree1.compare_tip_distances(tree1))  # identity case
0.0
>>> print(tree1.compare_tip_distances(tree2))
0.120492524415